Artificial Immune System for Solving Global Optimization Problems

In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model is used for global optimization problems. The model operates on four populations: Virgins, Effectors (CD4 and CD8) and Memory. Each of them has a different role, representation and procedures. We validate our proposed approach with a set of test functions taken from the specialized literature, we also compare our results with the results obtained by different bio-inspired approaches and we statistically analyze the results gotten by our approach.Fil: Aragon, Victoria Soledad. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo En Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis; ArgentinaFil: Esquivel, Susana C.. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaFil: Coello Coello, Carlos A.. CINVESTAV-IPN; Méxic

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades:Part B

Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time. In this two-part paper, we present a comprehensive survey of the research in evolutionary dynamic optimization for single-objective unconstrained continuous problems over the last two decades. In Part A of this survey, we propose a new taxonomy for the components of dynamic optimization algorithms, namely, convergence detection, change detection, explicit archiving, diversity control, and population division and management. In comparison to the existing taxonomies, the proposed taxonomy covers some additional important components, such as convergence detection and computational resource allocation. Moreover, we significantly expand and improve the classifications of diversity control and multi-population methods, which are under-represented in the existing taxonomies. We then provide detailed technical descriptions and analysis of different components according to the suggested taxonomy. Part B of this survey provides an indepth analysis of the most commonly used benchmark problems, performance analysis methods, static optimization algorithms used as the optimization components in the dynamic optimization algorithms, and dynamic real-world applications. Finally, several opportunities for future work are pointed out

免疫学的および進化的アルゴリズムに基づく改良された群知能最適化に関する研究

富山大学・富理工博甲第175号・楊玉・2020/3/24富山大学202

Artificial Immune Systems: Principle, Algorithms and Applications

The present thesis aims to make an in-depth study of adaptive identification, digital channel equalization, functional link artificial neural network (FLANN) and Artificial Immune Systems (AIS).Two learning algorithms CPSO and IPSO are also developed in this thesis. These new algorithms are employed to train the weights of a low complexity FLANN structure by way of minimizing the squared error cost function of the hybrid model. These new models are applied for adaptive identification of complex nonlinear dynamic plants and equalization of nonlinear digital channel. Investigation has been made for identification of complex Hammerstein models. To validate the performance of these new models simulation study is carried out using benchmark complex plants and nonlinear channels. The results of simulation are compared with those obtained with FLANN-GA, FLANN-PSO and MLP-BP based hybrid approaches. Improved identification and equalization performance of the proposed method have been observed in all cases

Design and Development of an Automated Mobile Manipulator for Industrial Applications

This thesis presents the modeling, control and coordination of an automated mobile manipulator. A mobile manipulator in this investigation consists of a robotic manipulator and a mobile platform resulting in a hybrid mechanism that includes a mobile platform for locomotion and a manipulator arm for manipulation. The structural complexity of a mobile manipulator is the main challenging issue because it includes several problems like adapting a manipulator and a redundancy mobile platform at non-holonomic constraints. The objective of the thesis is to fabricate an automated mobile manipulator and develop control algorithms that effectively coordinate the arm manipulation and mobility of mobile platform. The research work starts with deriving the motion equations of mobile manipulators. The derivation introduced here makes use of motion equations of robot manipulators and mobile platforms separately, and then integrated them as one entity. The kinematic analysis is performed in two ways namely forward & inverse kinematics. The motion analysis is performed for various WMPs such as, Omnidirectional WMP, Differential three WMP, Three wheeled omni-steer WMP, Tricycle WMP and Two steer WMP. From the obtained motion analysis results, Differential three WMP is chosen as the mobile platform for the developed mobile manipulator. Later motion analysis is carried out for 4-axis articulated arm. Danvit-Hartenberg representation is implemented to perform forward kinematic analysis. Because of this representation, one can easily understand the kinematic equation for a robotic arm. From the obtained arm equation, Inverse kinematic model for the 4-axis robotic manipulator is developed. Motion planning of an intelligent mobile robot is one of the most vital issues in the field of robotics, which includes the generation of optimal collision free trajectories within its work space and finally reaches its target position. For solving this problem, two evolutionary algorithms namely Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) are introduced to move the mobile platform in intelligent manner. The developed algorithms are effective in avoiding obstacles, trap situations and generating optimal paths within its unknown environments. Once the robot reaches its goal (within the work space of the manipulator), the manipulator will generate its trajectories according to task assigned by the user. Simulation analyses are performed using MATLAB-2010 in order to validate the feasibility of the developed methodologies in various unknown environments. Additionally, experiments are carried out on an automated mobile manipulator. ATmega16 Microcontrollers are used to enable the entire robot system movement in desired trajectories by means of robot interface application program. The control program is developed in robot software (Keil) to control the mobile manipulator servomotors via a serial connection through a personal computer. To support the proposed control algorithms both simulation and experimental results are presented. Moreover, validation of the developed methodologies has been made with the ER-400 mobile platform

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Fitness landscape-based analysis of nature-inspired algorithms

As the number of nature-inspired algorithms increases so does the need to characterise these algorithms. A rigorous process to characterise algorithms helps practitioners decide which algorithms may offer a good fit for their given problem. One approach is to relate the characteristics of a problem's associated fitness landscape with the performance of an algorithm. The aim of this thesis is to capitalise on the notion of fitness landscape characteristics as a technique for analysing algorithm performance, and to provide a novel algorithm- and problem-independent methodology that can be used to present the strengths and weaknesses of an algorithm. The methodology was tested by developing a portfolio of six nature-inspired algorithms commonly used to solve continuous optimisation problems. This portfolio includes the performance of these algorithms with parameters both “out of the box" and after they have been tuned using an automated tuning technique. Each of the algorithms shows a different “resilience" profile to the landscape characteristics, and responds differently to the tuning process. In order to provide a more practical way to utilise the portfolio an automated “ranking" methodology based on two machine learning techniques was developed. Using estimates of the fitness landscape characteristics on benchmark problems, the best algorithm to use is estimated, and compared with the actual performance of each algorithm. While results show that predicting algorithm performance is difficult, the results are promising, and show that this is an area worth exploring further. This methodology has significant advantages over the current practice of demonstrating novel algorithm performance on benchmark problems, most importantly offering a practical, generalised overview of the algorithm to a potential practitioner. Choosing to use a technique such as the one demonstrated here when presenting a novel algorithm could greatly ease the problem of algorithm selection

Program and Proceedings: The Nebraska Academy of Sciences 1880-2010

PROGRAM FRIDAY, APRIL 23, 2010 REGISTRATION FOR ACADEMY, Lobby of Lecture wing, Olin Hall Aeronautics and Space Science, Session A, Olin 249 Aeronautics and Space Science, Session B, Olin 224 Chemistry and Physics, Section A, Chemistry, Olin A Collegiate Academy, Biology Session A, Olin B Collegiate Academy, Chemistry and Physics, Session A, Olin 324 Biological and Medical Sciences, Session A, Olin 112 Biological and Medical Sciences, Session B, Smith Callen Conference Center Chemistry and Physics, Section B, Physics, Planetarium History and Philosophy of Science, Olin 325 Junior Academy, Judges Check-In, Olin 219 Junior Academy, Senior High REGISTRATION, Olin Hall Lobby NWU Health and Sciences Graduate School Fair, Olin and Smith Curtiss Halls Junior Academy, Senior High Competition, Olin 124, Olin 131 Aeronautics and Space Science, Poster Session, Olin 249 Teaching of Science and Math, Olin 325 MAIBEN MEMORIAL LECTURE, OLIN B Dr. Mark Greip, Vice-Chair, Department of Chemistry, University of Nebraska-Lincoln LUNCH, PATIO ROOM, STORY STUDENT CENTER (pay and carry tray through cafeteria line, or pay at NAS registration desk) Aeronautics Group, Conestoga Room Anthropology, Olin 111 Biological and Medical Sciences, Session C, Olin 112 Biological and Medical Sciences, Session D, Smith Callen Conference Center Chemistry and Physics, Section A, Chemistry, Olin A Chemistry and Physics, Section B, Physics, Planetarium Collegiate Academy, Biology Session A, Olin B Collegiate Academy, Biology Session B, Olin 249 Collegiate Academy, Chemistry and Physics, Session A, Olin 324 Junior Academy, Judges Check-In, Olin 219 Junior Academy, Junior High REGISTRATION, Olin Hall Lobby Junior Academy, Senior High Competition, (Final), Olin 110 Earth Science, Olin 224 Junior Academy, Junior High Competition, Olin 124, Olin 131 NJAS Board/Teacher Meeting, Olin 219 Junior Academy, General Awards Presentations, Smith Callen Conference Center BUSINESS MEETING, OLIN B SOCIAL HOUR for Members, Spouses, and Guests First United Methodist Church, 2723 N 50th Street, Lincoln, NE ANNUAL BANQUET and Presentation of Awards and Scholarships First United Methodist Church, 2723 N 50th Street, Lincoln, N

A survey of evolutionary continuous dynamic optimization over two decades – part A

Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time. In this two-part paper, we present a comprehensive survey of the research in evolutionary dynamic optimization for single-objective unconstrained continuous problems over the last two decades. In Part A of this survey, we propose a new taxonomy for the components of dynamic optimization algorithms, namely, convergence detection, change detection, explicit archiving, diversity control, and population division and management. In comparison to the existing taxonomies, the proposed taxonomy covers some additional important components, such as convergence detection and computational resource allocation. Moreover, we significantly expand and improve the classifications of diversity control and multi-population methods, which are under-represented in the existing taxonomies. We then provide detailed technical descriptions and analysis of different components according to the suggested taxonomy. Part B of this survey provides an indepth analysis of the most commonly used benchmark problems, performance analysis methods, static optimization algorithms used as the optimization components in the dynamic optimization algorithms, and dynamic real-world applications. Finally, several opportunities for future work are pointed out